Clustering algorithm incorporating density and direction

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Title: Clustering algorithm incorporating density and direction
Authors: Song, Yu-Chen
O'Grady, Michael J.
O'Hare, G. M. P. (Greg M. P.)
Wang, Wei
Permanent link: http://hdl.handle.net/10197/1346
Date: Dec-2008
Abstract: This paper analyses the advantages and disadvantages of the K-means algorithm and the DENCLUE algorithm. In order to realise the automation of clustering analysis and eliminate human factors, both partitioning and density-based methods were adopted, resulting in a new algorithm – Clustering Algorithm based on object Density and Direction (CADD). This paper discusses the theory and algorithm design of the CADD algorithm. As an illustration of its applicability, CADD was used to cluster real world data from the geochemistry domain.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE Computer Society
Copyright (published version): 2008 by The Institute of Electrical and Electronics Engineers, Inc.
Subject LCSH: Cluster analysis--Computer programs
Algorithms
Data mining
DOI: 10.1109/CIMCA.2008.34
Language: en
Status of Item: Peer reviewed
Is part of: Mohammadian, M. (ed.). Proceedings : 2008 International Conference on Computational Intelligence for Modelling, Control and Automation : CIMCA 2008, International Conference on Intelligent Agents, Web Technologies and Internet Commerce : IAWTIC 2008, International Conference on Innovation in Software Engineering : ISE 2008
Conference Details: Paper presented at the International Conference on Computational Intelligence for Modelling, Control and Automation (CIMCA 2008), 10-12 December 2008 - Vienna, Austria
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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